Results 281 to 290 of about 62,622 (323)

Detecting landscape forms using Fourier transformation and singular value decomposition (SVD)

Computers & Geosciences, 2009
Landscape structure is a main determinant of ecological landscape potentials. The basic differentiation of relief into depressions and elevations at deliberately chosen scales can be managed comfortably by the Fourier transformation. The automated extraction of these structures from an elevation map using Fourier transformation or singular value ...
Ralf Wieland, Claus Dalchow
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A singular value decomposition-based guided wave array signal processing approach for weak signals with low signal-to-noise ratios

, 2020
This paper presents a novel singular value decomposition (SVD)-based guided wave array signal processing approach for relatively weak signals, which are usually encountered in long-range inspections.
Peng Wang, Wensong Zhou, Hui Li
semanticscholar   +1 more source

Singular Value Decomposition (SVD)-Based Video Watermarking

2018
This chapter presents using singular value decomposition (SVD)-based video watermarking approaches. The experimental results of these approaches are also demonstrated in this chapter.
Ashish M. Kothari, Vedvyas Dwivedi, Rohit M. Thanki   +2 more
openaire   +1 more source

Self-supervised Knowledge Distillation Using Singular Value Decomposition

European Conference on Computer Vision, 2018
To solve deep neural network (DNN)’s huge training dataset and its high computation issue, so-called teacher-student (T-S) DNN which transfers the knowledge of T-DNN to S-DNN has been proposed.
Seunghyun Lee, Dae Ha Kim, B. Song
semanticscholar   +1 more source

Accelerating the Singular Value Decomposition of Rectangular Matrices with the CSX600 and the Integrable SVD

2007
We propose an approach to speed up the singular value decomposition (SVD) of very large rectangular matrices using the CSX600 floating point coprocessor. The CSX600-based acceleration board we use offers 50GFLOPS of sustained performance, which is many times greater than that provided by standard microprocessors.
Yusaku Yamamoto, Takeshi Fukaya, Takashi Uneyama, Masami Takata, Kinji Kimura, Masashi Iwasaki, Yoshimasa Nakamura   +6 more
openaire   +1 more source

Face recognition technique using Gabor wavelets and Singular Value Decomposition (SVD)

2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), 2014
Gabor wavelets (also known as Gabor filters) and Singular Value Decomposition (SVD) have been exploited extensively in the area of face recognition. In this paper, a face recognition system is developed combining features extracted using both Gabor wavelets and SVD.
Lim Song Li, Norashikin Yahya
openaire   +1 more source

DALM-SVD: Accelerated sparse coding through singular value decomposition of the dictionary

2014 IEEE International Conference on Image Processing (ICIP), 2014
Sparse coding techniques have seen an increasing range of applications in recent years, especially in the area of image processing. In particular, sparse coding using l 1 -regularization has been efficiently solved with the Augmented Lagrangian (AL) applied to its dual formulation (DALM).
Hugo R. Gonçalves, Miguel V. Correia 0002, Xin Li 0001, Aswin C. Sankaranarayanan, Vítor Tavares   +4 more
openaire   +1 more source

New technique for face recognition based on Singular Value Decomposition (SVD)

2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech), 2016
In this paper, we describe a texture analysis which plays an important role in face analysis. Many studies have used texture image to characterize a face. Local Binary Pattern (LBP) coding is a state-of-the-art technique characterized by it's simplicity and efficiency.
El Mahdi Barrah, Said Safi, Abdessamad Malaoui   +2 more
openaire   +1 more source

Passivity Verification and Macromodel Interpolation Using Singular Value Decomposition (SVD)

2015 IEEE Workshop on Microelectronics and Electron Devices (WMED), 2015
Approximation of the Rational Function (RF) order plays a key role in passivity checking of large interconnect memory design. RF approximation can be estimated using the Least- square solutions; the Singular Value Decomposition (SVD) is used to solve large order macromodels.
Dalia Elgamel, Roy Greeff, David Ovard
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Singular Value Decomposition (SVD) and Polar Form

2001
In this section we assume that we are dealing with a real Euclidean space E. Let \( f : E \rightarrow E \) be any linear map. In general, it may not be possible to diagonalize f. We show that every linear map can be diagonalized if we are willing to use two orthonormal bases. This is the celebrated singular value decomposition (SVD).
openaire   +1 more source